May 21, 2021
I’ve released a new > Github repository <, > dataset <, and > ArXiv preprint < for a paper I submitted to a journal, titled “BodyPressure – Inferring Body Pose and Contact Pressure from a Depth Image.” It shows that a low-cost camera might be used to identify peak pressure regions on a person in bed, which is applicable to the problem of preventing pressure injuries in a healthcare setting. I also filed a provisional patent for it, so if you are interested in commercializing it, please contact me. Here is the abstract:
Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method that infers contact pressure between a human body and a mattress from a depth image. Specifically, we focus on using a depth image from a downward facing camera to infer pressure on a body at rest in bed occluded by bedding, which is directly applicable to the prevention of pressure injuries in healthcare. Our approach involves augmenting a real dataset with synthetic data generated via a soft-body physics simulation of a human body, a mattress, a pressure sensing mat, and a blanket. We introduce a novel deep network that we trained on an augmented dataset and evaluated with real data. The network contains an embedded human body mesh model and uses a white-box model of depth and pressure image generation. Our network successfully infers body pose, outperforming prior work. It also infers contact pressure across a 3D mesh model of the human body, which is a novel capability, and does so in the presence of occlusion from blankets.
February 24, 2020
I’m very happy to share that my paper, “Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image Using Synthetic Data” was accepted at CVPR 2020. Here is the abstract:
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